Solution of Inverse-Interpolation Problem by Power-Excitation Feedforward Neural Networks

Journal of South China University of Technology (Natural Science Edition) ›› 2009, Vol. 37 ›› Issue (5): 68-72.

• Electronics, Communication & Automation Technology • Previous Articles Next Articles

Solution of Inverse-Interpolation Problem by Power-Excitation Feedforward Neural Networks

Zhang Yu-nong¹ Zeng Yan² Zhong Tong-ke¹ Tang Zhi-shuang³ Mo Hong-qiang⁴

1. School of Information Science and Technology, Sun Yat-Sen University, Guangzhou 510275, Guangdong, China; 2. School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, Guangdong, China; 3. School of Software, Sun Yat-Sen University, Guangzhou 510275, Guangdong, China; 4. School of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China

Received:2008-05-04 Revised:2008-07-17 Online:2009-05-25 Published:2009-05-25
Contact: 张雨浓（1973-），男，博士，教授，博士生导师，主要从事神经网络和机器人研究． E-mail:zhynong@mail．sysu．edu．cn
About author:张雨浓（1973-），男，博士，教授，博士生导师，主要从事神经网络和机器人研究．
Supported by:
国家自然科学基金资助项目（60643004,60775050）;中山大学科研启动费、后备重点课题

Abstract

Abstract:

By using numerical algorithms to solve inverse-interpolation problems, the accuracy of positive solution is influenced by the choice of initial values, and the computational speed is slow. In order to solve the problems, a power-excitation feedforward neural network is employed to solve inverse-interpolation problems. As the adopted neural network is only suitable for the inverse-interpolation problem with one-to-one mapping but not for that with multiple-to-one mapping, the timing control condition is introduced into the neural network to construct a timing power-excitation feedforward neural network model. Theoretically derived and simulated results indicate that the constructed neural network effectively solve the inverse-interpolation problems with both one-to-one and multiple-to- one mappings.

Key words: neural network, direct weight determination, inverse interpolation, time sequence

Zhang Yu-nong Zeng Yan Zhong Tong-ke Tang Zhi-shuang Mo Hong-qiang. Solution of Inverse-Interpolation Problem by Power-Excitation Feedforward Neural Networks[J]. Journal of South China University of Technology (Natural Science Edition), 2009, 37(5): 68-72.

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Solution of Inverse-Interpolation Problem by Power-Excitation Feedforward Neural Networks

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